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ACEEE Int. J. on Information Technology, Vol. 01, No. 01, Mar 2011 

Marathi Isolated Word Recognition System using 
MFCC and DTW Features 

Bharti W. Gawali 1 , Santosh Gaikwad 2 , Pravin Yannawar 3 , Suresh C.Mehrotra 4 

i,2,3 ^Department of Computer Science & Information Technology, 

Dr.Babasaheb Ambedkar Marathwada University, 

Aurangabad. 431001(MS) India. 

Address: l bharti rokade@yahoo.co.in , , 2santosh. gaikwadcsit @ gmail.com 

3praviny annawar @ gmail . com , 4mehrotra suresh@yahoo.com 



Abstract — This paper presents a Marathi database and isolated 
Word recognition system based on Mel-frequency cepstral 
coefficient (MFCC), and Distance Time Warping (DTW) as 
features. For the extraction of the feature, Marathi speech 
database has been designed by using the Computerized Speech 
Lab. The database consists of the Marathi vowels, isolated 
words starting with each vowels and simple Marathi sentences. 
Each word has been repeated three times by the 35 speakers. 
This paper presents the comparative recognition accuracy of 
DTW and MFCC. 

Index Terms— CSL, MFCC, DTW, Spectrogram, Speech 
Recognition and statistical method 

I.. Introduction 

The Speech is the most prominent and natural form of 
communication between humans. There are various spoken 
Languages thought the world [1]. Marathi is an Indo- Aryan 
Language, spoken in western and central India. There are 
90 million of fluent speakers all over world [2]. However; 
there is lot of scope to develop systems using Indian 
languages which are of different variations. Some work is 
done in this direction in isolated Bengali words, Hindi and 
Telugu [3]. The amount of work in Indian regional languages 
has not yet reached to a critical level to be used it as real 
communication tool, as already done in other languages in 
developed countries. Thus, this work was taken to focus on 
Marathi language. It is important to see that whether Speech 
Recognition System for Marathi can be carried out similar 
pathways of reaserch as carried out in English [4, 5]. In this 
paper we are presenting work consists of the creation of 
Marathi speech database and its speech recognition system 
for isolated words. 

The paper is divided into five sections, Section 1, gives 
Introduction, Section 2 deals with details of creating Marathi 
speech database , section 3,focuses on Recognition of isolated 
words using MFCC and DTW, Section 4 , covers results and 
conclusion followed by section 5 with the References. 

Dr.BhaitiW.Gawali 

Associate Professor 

Department of Computer Science & Information Technology 

Dr.Babasaheb Ambedkar Marathwada University Aurangabad. 

Email. bhartij'okadeff yahoo. co. in 

DST Project Sanction: SR/FTP/ETA-0009/2010 



II. MARATHI SPEECH DATABASE 

For accuracy in the speech recognition, we need a 

collection of utterances, which are required for training and 
testing. The Collection of utterances in proper manner is 

called the database. 

The generation of a corpus of Marathi Vowels, words 

and sentences as well as the collection of speech data are 

described below. The age group of speakers selected for the 

collection of database ranges from 22 to 35. Mother tongue 

of all the speakers was Marathi. The total number of speakers 

was 35 out of which 17 were Females and 18 were Males. 

The vocabulary size of the database consists of 

• Marathi Vowels: 105 samples 

• Isolated words stating with each vowel: 
420 Samples 

• Sentences: 175 samples. 

A. Acquisition setup 

To achieve a high audio quality the recording took place 
in the 10 X 10 rooms without noisy sound and effect of echo. 
The Sampling frequency for all recordings was 1 1025 Hz in 
the Room temperature and normal humidity. The speaker 
were Seating in front of the direction of the microphone with 
the Distance of about 12-15 cm [6], The speech data is 
collected with the help of Computerized speech laboratory 
(CSL) using the single channel. The CSL is most advanced 
analysis system For speech and voice. It is a complete 
hardware and software system with specifications and 
performance. It is an input/output recording device for a PC, 
which has special features for reliable acoustic measurements 
[7]. 

B. Vowels, Words and Sentences corpus 

Marathi language uses Devanagari, a character based 
script. A character represents one vowel and zero or more 
consonants. There are 12 vowels and 36 consonants present 
in Marathi languages as shown in figure La [8]. We have 
recorded vowels, isolated words starting from each vowel 
and the simple sentences which are used for communication 
in Government offices shown in figure Lb. All sentences 
are interrogative and containing 5 to 6 words. The speech 
signals have been stored in form of wav file. Each vowels, 
words and sentences have been separated using CSL software 
and stored and given the labels of spoken words to the 
different files and folders. 



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ACEEE Int. J. on Information Technology, Vol. 01, No. 01, Mar 2011 











Vowels 

3T, 3TT, $, #, S, 3s H, £, 3ft, 3ft, 3f, 3T: 

a, a, i, I, u, u, e, ai, o, au, am, ah 




Consonants 

^, ^, *T, *T, W,^ *5, ^, 51, 3T, 
ka, kha, ga, gha, ria, ca, cha, ja, jha, fin, 
Z, 5, ?, 5, ^T, H, 9T, BT, SI, ^T, 

ta, tha, da, dha, rta, ta, tha, da, dha, na, 
U, if>, of, 3T, W, 3j, *, <*, sr, 9T, 
pa, pha, ba, bha, ma, ya, ra, la, va, sa, 

^, H, F* aS, ft, ^T 
Sa, sa, ha, J, ks gh 












Figerla. The vowels and consonant in Marathi 
corresponding to English language 













Vowels 




3T, 3TT, ^, f, 5, S, 1J, 3, 3lt, 3?1, 3T, 3T: 






a, a, i, I, u, u, e, ai, o, au, am, ah 






Consonants 








^, *sT, *T, W, S 1 ,^, e5, oT, 5T, 3T, 










ka, kha, ga, gha, ria, ca, cha, ja, jha, fin. 










Z, 3, s, s; or, H, ar, 5, ir, ^T, 










ta, tha, da, dha, na, ta, tha, da, dha, na, 










<I, *f>, S, W, 3T, 31, T, <*, ST, ST, 










pa, pha, ba, bha, ma, ya, ra, la, va, sa, 










*T, H, F, o5, ft, *T 










?a, sa, ha, J, ft? #« 














Figerla. The vowels and consonant in Marathi 




corresponding to English language 



III. WORD RECOGNITION SYSTEM 

There are several kinds of parametric representation of 
the acoustic signals. Among of them the Mel-Frequency 
cepstral Coefficient (MFCC) is most widely used [9]. There 
are many work on MFCC, on the improvement and accuracy 
in recognition [10]. The recognition rate achieved by MCC, 
LPC and Combined are given 87.5%, 91.61% and 84.6% 
respectively for 312 number of testing patterns [11] .We have 
developed the recognition system using MFCC and 
DTW.paragraphs must be indented. All paragraphs must be 
justified, i.e. both left -justified and right-justified. 

A. Mel Frequency Cepstral Coefficient 

It consists of various steps described below. 

Speech Signal 

The excitation signal is spectrally shaped by a vocal tract 
Equivalent filter. The outcome of this process is the sequence 
of exciting signal called speech 



Pre-emphasis 

The speech is first pre-emphasis with the pre-emphasis 
filter 1-az-l to spectrally flatten the signal. 

Framing and Windowing 

A speech signal is assumed to remain stationary in periods 
of approximately 20 ms. Dividing a discrete signal s[n] into 
frames in the time domain truncating the signal with a 
window function w[n]. This is done by multiplying the signal, 
consisting of N samples, . The frame is shifted 10 ms so that 
the overlapping between two adjacent frames is 50% to avoid 
the risk of losing the information from the speech signal. 
After dividing the signal into frames that contain nearly 
stationary signal blocks, the windowing function is applied. 

Fourier Transform 

To obtain a good frequency resolution, a 512 point Fast 
Fourier Transform (FFT) is used [12, 13] 

Mel-Frequency Filter Bank 

A filter bank is created by calculating a number of peaks, 
Uniformly spaced in the Mel-scale and then transforming 

the back to the normal frequency scale where they are used 

a speaks for the filter banks. 

Discrete Cosine Transform 

As the Mel-cepstrum coefficients contain only real parts, 
the Discrete Cosine Transform (DCT) can be used to achieve 
the Mel- cepstrum coefficients. There were 24 Coefficients 
out of that only 1 3 coefficients have been selected for the 
recognition system. 

Distance Measures 

There are some commonly used distance measures i.e. 
Euclidean Distance. Euclidian Distance of two vectors x and 
p is used measured. The result of MFCC on the vector of 
words starting with' 3sf'are presented in Table 1. 

B. Dynamic Time Warping 

The simplest way to recognize an isolated word sample 
is to compare it against a number of stored word templates 
and determine the best match [14,15]. DTW is an instance of 
the general class of algorithms and known as dynamic 
programming .its time and space complexity is merely linear 
in duration of speech sample and the vocabulary size. The 
algorithm makes a single pass though a matrix of frame scores 
while computing locally optimized segment of the global 
alignment path. The dynamic time warping algorithm 
provides a procedure to align in the test and reference patterns 
to give the average distance associated with the optimal 
warping path. The results of DTW are given in table 2. 

IV. RESULTS AND CONCLUSION 

The aim here is to compare the performance of MFCC 
(where 13 coefficients are used), and DTW. The speech data 
used in this experiment are the isolated words starting from 
vowel" in Marathi, spoken by female speakers. The test 
pattern is compared with the reference pattern to get the best 
Match. The symmetric form of DTW algorithm is used to 
optimally align in time the test and reference patterns and 



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Acknowledgment 
The authors would like to thank the university authorities 



and to give average distance associated with optimal warping 
path. The recognition system uses the utterance of the spoken 

word For training and the remaining utterances for testing. for providing i nfrastructure to carry out experiment This 

The Reference patterns are created for each word in the , , , . , . t^ot^ j t- » t, , , 

, , ,,-, , , r work has been supported by DST under Fast Track Scheme 

vocabulary from the data in the training set by averaging (alter . , , ._ . , _; , . . , 

... . . . . ... entitled as Design and Development of Marathi Speech 

dynamic time warping) the patterns or utterances ol the same ,7 

, T,, .■ ... • . j . Interface System . 

word. The comparative recognition accuracy is presented in J 

Table 3. 



TABLE I 
THE DISTANCE MATRIX OF TWO SUBJECTS USTNGMFCC 




Ablryas 


Ajay 


Akara 


Amar 


Ananas 


Ati 


Avidya 


AKtryas 


0.03 


0.809S 


0.9827 


1.1824 


1.2455 


1.6888 


0.5257 


Ajay 


0.3878 


0.0839 


1.0403 


1.240 


1.3031 


1.7464 


0.1114 


Akara 


0.5198 


0.9996 


0.0385 


1.3722 


1.4353 


1.8786 


0.7155 


Amar 


0.1813 


0.5155 


0.6844 


0.1783 


0.9471 


1.3904 


0.2274 


ArLams 


0.0943 


0.5740 


0.7470 


0.9466 


0.4530 


1.4530 


0.2899 


Ati 


0.2978 


0.2197 


0.3297 


0.5923 


1.0982 


0.0346 


0.09S5 


Avidya 


0.3837 


0.8635 


1.0364 


1.24 


1.2991 


1.7424 


0.0702 







TABLE n 
THE DISTANCE MATRIX OF TWO SUBJECTS USING DTW 





Abliyas 


Ajay 


Akara 


Amar 


Ananas 


Art 


Avidya 


Abhyas 


24.12 


23.20 


27.53 


27.93 


2S.96 


29.52 


26.89 


Ajay 


24.21 


24.01 


34.04 


23.07 


30.25 


28.68 


27.67 


Akara 


22.65 


27.30 


20.32 


24.91 


26.77 


23.39 


21.78 


Amar 


29.19 


24.70 


28.43 


24.04 


25.78 


29.20 


32.12 


Ananas 


31.03 


23.78 


25.94 


26.11 


20.25 


20.52 


29.10 


Ati 


28.60 


29.89 


24.47 


26.73 


27 2*7 


24.27 


30.39 


Avidya 


24.42 


24.83 


;/j ^6 


27.40 


25.89 


30.11 


22.49 



TABLE LLT 

COMPARATIVE RECOGNITION ACCURACY FOR. MFCC AND 

DTW 



Algorithm 


Vector 
size 


Percentage of 
variance 


Recognition 
accuracy 


MFCC 


49 


5 ^ *; 


94.65 


DTW 


49 


26.75 


73- .2.5 



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©2011 ACEEE 
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